code: def score(self, parallel_corpus):
      # containers
      count = [0, 0, 0, 0]
      clip_count = [0, 0, 0, 0]
      r = 0
      c = 0
      weights = [0.25, 0.25, 0.25, 0.25]
  
      # accumulate ngram statistics
      for hyps, refs in parallel_corpus:
          hyps = [hyp.split() for hyp in hyps]
          refs = [ref.split() for ref in refs]
          for hyp in hyps:
              for i in range(4):
                  # accumulate ngram counts
                  hypcnts = Counter(ngrams(hyp, i + 1))
                  cnt = sum(hypcnts.values())
                  count[i] += cnt
  
                  # compute clipped counts
                  max_counts = {}
                  for ref in refs:
                      refcnts = Counter(ngrams(ref, i + 1))
                      for ng in hypcnts:
                          max_counts[ng] = max(
                              max_counts.get(ng, 0), refcnts[ng]
                          )
                  clipcnt = dict(
                      (ng, min(count, max_counts[ng]))
                      for ng, count in hypcnts.items()
                  )
                  clip_count[i] += sum(clipcnt.values())
  
              # accumulate r & c
              bestmatch = [1000, 1000]
              for ref in refs:
                  if bestmatch[0] == 0:
                      break
                  diff = abs(len(ref) - len(hyp))
                  if diff < bestmatch[0]:
                      bestmatch[0] = diff
                      bestmatch[1] = len(ref)
              r += bestmatch[1]
              c += len(hyp)
  
      # computing bleu score
      p0 = 1e-7
      bp = 1 if c > r else math.exp(1 - float(r) / float(c))
      p_ns = [
          float(clip_count[i]) / float(count[i] + p0) + p0
          for i in range(4)
      ]
      s = math.fsum(
          w * math.log(p_n) for w, p_n in zip(weights, p_ns) if p_n
      )
      bleu = bp * math.exp(s)
      return bleu * 100
  
imports: import math, logging, copy, json
  from collections import Counter, OrderedDict
  from nltk.util import ngrams
  from .None import ontology
  from .clean_dataset import clean_slot_values
label_desc: Use image node_id 2 for calling the BLEUScorer obj's underlying member method code with example usage: obj.score(parallel_corpus) and returns: unknown